Bibliographic record
Abstract
This paper explores how TEMU, an emerging global e-commerce marketplace, shapes merchant-consumer relations through its “Refund Without Return” (hereafter refund-without-return) policy and associated penalty policy. Using digital ethnography, I analyze approximately 100 posts and videos sampled from merchant and customer content on TikTok, YouTube, Reddit, Meta platforms, and Chinese online forums between 2022 and 2024, together with TEMU’s public documents and media coverage. Thematic analysis based on platform capitalism and digital labour theory shows that the refund-without-return policy and the five-fold penalty mechanism are, in fact, a unilateral agreement that shifts financial risks and operational burdens to small businesses, leading to unstable income, heavy debt, and widespread anxiety and exhaustion. Meanwhile, social media communities spread “tips,” “secrets,” and success stories about how to take advantage of refund policies, packaging these practices as smart consumer behaviour and “happy shopping,” therefore diffusing individual responsibility. Bringing these strands together, I conceptualize TEMU’s refund-without-return policy as a dual exploitation mechanism, in which platform governance and online consumer behaviour jointly erode merchants’ bargaining power and economic security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".